Access WAPOSAL Waves - Ocean Hackathon

* Original Data Source: https://opensciencedata.esa.int/products/waposal-waves/collection
* Reference:  https://opensciencedata.esa.int/products/waposal-waves/collection
* OSC entry: https://opensciencedata.esa.int/products/waposal-waves/collection
* License: CC-BY-SA-4.0

import xarray as xr
data_url = 'https://s3.waw4-1.cloudferro.com/EarthCODE/OSCAssets/waposal/MT-S3A.zarr'
ds = xr.open_zarr(data_url, decode_times=False, zarr_format=2)
ds

ds.trajectory_info.values

array(['P_RES_S3A_SR_1_SRA_A__20160313T200006_20160313T200020_20180203T001443_0013_002_014_GPOD_SAR_R_NT_003.nc',
       'P_RES_S3A_SR_1_SRA_A__20160406T080605_20160406T085634_20180203T005835_3029_002_349_GPOD_SAR_R_NT_003.nc',
       'P_RES_S3A_SR_1_SRA_A__20160406T094704_20160406T103733_20180203T012114_3029_002_350_GPOD_SAR_R_NT_003.nc',
       ...,
       'P_RES_S3A_SR_1_SRA_A__20221231T100948_20221231T110018_20230126T130756_3029_094_008_GPOD_SAR_O_NT_004.nc',
       'P_RES_S3A_SR_1_SRA_A__20221231T192514_20221231T201544_20230126T213108_3029_094_013_GPOD_SAR_O_NT_004.nc',
       'P_RES_S3A_SR_1_SRA_A__20221231T210613_20221231T215642_20230126T230552_3028_094_014_GPOD_SAR_O_NT_004.nc'],
      shape=(10339,), dtype='<U103')

import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature

trj_indx = 3
lons = ds.isel(trajectory=trj_indx)['longitude_20Hz'].compute()
lats = ds.isel(trajectory=trj_indx)['latitude_20Hz'].compute()
data_to_plot = ds.isel(trajectory=trj_indx)['SWH_20Hz'].compute() 

fig = plt.figure(figsize=(12, 8))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.add_feature(cfeature.LAND, facecolor='lightgray')
ax.add_feature(cfeature.OCEAN, facecolor='azure')
ax.add_feature(cfeature.COASTLINE, linewidth=0.5)
ax.add_feature(cfeature.BORDERS, linestyle=':', linewidth=0.5)
ax.gridlines(draw_labels=True, dms=True, x_inline=False, y_inline=False, color='gray', linestyle='--')

scatter = ax.scatter(
    lons, lats, 
    c=data_to_plot, 
    cmap='viridis',     
    s=15,               
    transform=ccrs.PlateCarree(), 
    alpha=0.8       
)

cbar = plt.colorbar(scatter, ax=ax, orientation='vertical', pad=0.05, shrink=0.7)
cbar.set_label('Significant Wave Height (SWH) - 20Hz')

ax.set_extent([
    lons.min() - 2, lons.max() + 2, 
    lats.min() - 2, lats.max() + 2
], crs=ccrs.PlateCarree())

plt.show()

Timeseries¶

import xarray as xr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

lat_location = 33
lon_location = 30
vicinity_degree = 0.5


data_url = 'https://s3.waw4-1.cloudferro.com/EarthCODE/OSCAssets/waposal/MT-S3A.zarr'
ds = xr.open_zarr(data_url, decode_times=False, zarr_format=2)

time = ds['TAI_Time_20Hz']
lons = ds['longitude_20Hz']
lats = ds['latitude_20Hz']
SWH = ds['SWH_20Hz']


# Geographic mask
location_mask = (
    (np.abs(lons - lon_location) < vicinity_degree)
    & (np.abs(lats - lat_location) < vicinity_degree)
).compute()

# Apply geographic mask
Hs_location = SWH.where(location_mask)

# Get time values
time_values = time.compute().values[:,0]

# Convert seconds since 1 Jan 2000 to datetime
dates = pd.to_datetime(
    time_values,
    unit='s',
    origin='2000-01-01'
)

# Calculate mean SWH for each timestamp
mean_swh = Hs_location.mean(dim='obs').compute()


valid = np.isfinite(mean_swh.values)
mean_swh = mean_swh[valid]
dates = dates[valid]

# Plot
fig, ax = plt.subplots(figsize=(12, 5))

ax.plot(
    dates,
    mean_swh.values,
    '-o',
    color='r',
    markersize=4,
    linewidth=0.8
)


ax.set_xlabel('Time')
ax.set_ylabel('Mean SWH (m)')
ax.set_title('Mean Significant Wave Height near 30°E, 33°N')

# Month + year
ax.xaxis.set_major_locator(mdates.MonthLocator(interval=3))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))

plt.xticks(rotation=45)
plt.grid(True)
plt.tight_layout()
plt.show()